<div dir="ltr">Dear Guido,<div><br></div><div>Thank you for your prompt reply! </div><div><br></div><div>It took me some time to fix it and react to your email, but I did manage in the end to fit the Poisson-normal model as described in Stijnen (2010), which led to much more logical effect estimates in my meta-analysis.</div><div>I didnt quite succeed to work it out with the Meta package due to some errors in the GLMM function and changed the decimal places manually (x100) in Metafor to obtain the numbers as %.</div><div>As you can see in the attachment, I also obtained the 0.73 value with Poisson (see attachment).</div><div><br></div><div>Also, maybe Wolfgang knows an simpler way to transform Forest plot values in Metafor also to achieve numbers per 100 pt yrs?</div><div><br></div><div>I also checked out the Meta package and does look super neat in the way it presents the individual study estimates in grey colour (makes the 95%CI visible in large studies) and also automatically gives you the weighing of studies and the measure of heterogeneity (all these I had to add manually). It definitely got my attention and I shall try it out for my next project.</div><div><br></div><div>Thank you once again for your great help!</div><div><br></div><div>Regards,</div><div><br></div><div>Todor</div><div>
<br></div></div><div class="gmail_extra"><br><div class="gmail_quote">On 11 January 2018 at 18:25, Guido Schwarzer <span dir="ltr"><<a href="mailto:sc@imbi.uni-freiburg.de" target="_blank">sc@imbi.uni-freiburg.de</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
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Dear Todor,<br>
<br>
I cannot comment (in detail) on using <b>metafor</b>, however,
using <a href="http://transf.ipft.hm" target="_blank">transf.ipft.hm</a>() is surely wrong, as this is for
back-transformation of proportions, not incidence rates. You have to
use transf.iirpf() for the inverse of the Freeman-Tukey
transformation of incidence rates. Note, you have to use the
function with two "i"s where the first "i" stands for "inverse" and
the second "i" stands for "incidence". Using transf.irpf() with one
"i" would be wrong.<br>
<br>
<br>
Alternatively, you could use function metarate() from <b>meta</b>
which does the back-transformation automatically for you.
Furthermore, metarate() has an argument 'irscale' to display results
are events per x person-years. e.g., 'irscale = 100' to report
events per 100 person-years.<br>
<br>
m.irft <- metarate(xi, ti, data = dat, studlab = study, sm =
"IRFT", method.tau = "PM", irscale = 100, prediction = TRUE)<br>
summary(m.irft)<br>
forest(m.irft, xlim = c(0, 5))<br>
<br>
As you see in the attached forest plot, there is a problem with the
Freeman-Tukey (back-)transformation:<br>
the overall estimates (fixed effect and random effects) are very
small which does not make much sense. This results from problems
with the back-transformation in this setting with many studies with
zero events.<br>
<br>
Therefore, I would recommend to use a different method for the
meta-analysis. Personally, I would use metarate() with argument
'method = "GLMM"' which conducts a random intercept Poisson
regression model (Stijnen et al. 2010, section 3.3). Note,
metarate() calls rma.glmm() from <b>metafor</b> internally to
conduct the Poisson regression.<br>
<br>
The pooled results for the Poisson regression<br>
<br>
0.73 events (fixed effect) and 0.70 events (random effects) per 100
person-years<br>
<br>
look much more plausible to me than the results for the
Freeman-Tukey method<br>
<br>
0.15 events (fixed effect) and 0.16 events (random effects) per 100
person-years.<br>
<br>
Best wishes,<br>
Guido<br>
<br>
<br>
Reference:<br>
<br>
Stijnen, T., Hamza, T.H. & Ozdemir, P., 2010, Random effects
meta-analysis of event outcome in the framework of the generalized
linear mixed model with applications in sparse data, Statistics in
Medicine, 29(29), pp. 3046-67<span class="HOEnZb"><font color="#888888"><br>
<pre class="m_-1675198762356200682moz-signature" cols="72">--
Dr. Guido Schwarzer
Institute of Medical Biometry and Statistics,
Faculty of Medicine and Medical Center - University of Freiburg
Postal address: Stefan-Meier-Str. 26, D-79104 Freiburg
Phone: <a href="tel:+49%20761%202036668" value="+497612036668" target="_blank">+49/761/203-6668</a>
Mail: <a class="m_-1675198762356200682moz-txt-link-abbreviated" href="mailto:sc@imbi.uni-freiburg.de" target="_blank">sc@imbi.uni-freiburg.de</a>
Homepage: <a class="m_-1675198762356200682moz-txt-link-freetext" href="http://www.imbi.uni-freiburg.de" target="_blank">http://www.imbi.uni-freiburg.<wbr>de</a>
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